key: cord-0826108-nhg9lp4v authors: Hodgson, Ashley; Bernardin, Thomas; Westermeyer, Benjamin; Hagopian, Ella; Radtke, Tyler; Noman, Ahmed title: Development of a specialty intensity score to estimate a patient's need for care coordination across physician specialties date: 2021-05-24 journal: Health Sci Rep DOI: 10.1002/hsr2.303 sha: 7e9ef73e85dc6b1ee4438b920af9839a004bac61 doc_id: 826108 cord_uid: nhg9lp4v BACKGROUNDS AND AIMS: This article develops a Specialty Intensity Score, which uses patient diagnosis codes to estimate the number of specialist physicians a patient will need to access. Conceptually, the score can serve as a proxy for a patient's need for care coordination across doctors. Such a measure may be valuable to researchers studying care coordination practices for complex patients. In contrast with previous comorbidity scores, which focus primarily on mortality and utilization, this comorbidity score approximates the complexity of a patient's the interaction with the health care system. METHODS: We use 2015 inpatient claims data from the Centers for Medicare and Medicaid Services to model the relationship between a patient's diagnoses and physician specialty usage. We estimate usage of specialist doctors by using a least absolute shrinkage and selection operator Poisson model. The Specialty Intensity Score is then constructed using this predicted specialty usage. To validate our score, we test its power to predict the occurrence of patient safety incidents and compare that with the predictive power of the Charlson comorbidity index. RESULTS: Our model uses 127 of the 279 International Classification of Disease, 10th Revision, Clinical Modification (ICD‐10‐CM) diagnosis subchapters to predict specialty usage, thus creating the Specialty Intensity Score. This score has significantly greater power in predicting patient safety complications than the widely used Charlson comorbidity index. CONCLUSION: The Specialty Intensity Score developed in this article can be used by health services researchers and administrators to approximate a patient's need for care coordination across multiple specialist doctors. It, therefore, can help with evaluation of care coordination practices by allowing researchers to restrict their analysis of outcomes to the patients most impacted by those practices. Health services researchers and policy-makers have increasingly acknowledged the importance of improving care coordination for patients with multiple chronic conditions. 1,2 Multiple chronic condition patients spend 65% of all health care dollars and 95% of Medicare dollars. 3 Yet, these patients receive substandard care, 4 often due to complications of the coordination process. 5 One definition of care coordination is "the deliberate organization of patient care activities between two or more participants (including the patient) involved in a patient's care to facilitate the appropriate delivery of health care services." 6 The complexity of these patient's interaction with the health care system may put the patient's health at greater risk, particularly if different doctors treating them provide conflicting care plans or fail to communicate well with each other or with the patient. For these reasons, care coordination has become a focus among policy-makers and health care administrators alike. The majority hospitals employ a variety of care management tools and techniques such as case managers, predictive analytic tools, checklists, visit summaries, conversations prompts in the medical records, and more. 7 Insurers incentivize good care coordination practices through bundled payment and pay-forperformance systems, although evidence is mixed about the effectiveness of these at improving care coordination. [8] [9] [10] [11] The Affordable Care Act included incentives for the development of Accountable Care Organizations in part as a way of promoting innovation and accountability for care coordination practices. 12 And not just the United States, but countries across the globe have highlighted care coordination as a goal. [13] [14] [15] [16] Recognizing the importance of developing measurements of care coordination, the Agency for Healthcare Research and Quality undertook a project called the Care Coordination Measures Atlas, which summarized the available literature and identified 64 existing instruments for measuring the quality of care coordination. 17 This project was most recently updated in June 2014, when it introduced a section on care coordination measures that can be constructed from electronic health records (EHR). Measurements that rely on EHR have numerous advantages, including the limited data collection burden and ease of aggregating across broader populations. 18 While the project uncovered 26 measures of care coordination developed from EHR data, all of them required more specific information beyond simply the International Classification of Disease, 10th Revision, Clinical Modification (ICD-10-CM) codes that make up the bulk of many widely available data sets. Since the release of the Care Coordination Measures Atlas, a 2018 Veterans Affairs conference focused on care coordination, and published a report identifying gaps in the available measures of care coordination. 19 This report highlighted a continued need to identify which patients could benefit most from care coordination practices. Our project seeks to fill that gap identified by Agency for Healthcare Research and Quality and the 2018 Veterans Affairs coordination conference by providing a measure of a patient's need for care coordination that can be used by researchers with access to widely available datasets with ICD-10-CM patient diagnostic codes. Specialty Intensity Score aims to allow researchers to identify which patients are most likely to require care from multiple medical specialists. The score serves as an estimate for a patient's need for care coordination and can be combined with coordination-sensitive outcome measures to assess the quality of carecoordination within and across health care facilities. By separating out patients likely to need the most care coordination, researchers can study coordination-sensitive outcomes with greater precision. Coordinationsensitive outcomes may include death rate among patients with lowmortality diagnoses, hospital-acquired infections, and hospital readmissions. To check for advancements in the field since the 2014 Agency for Healthcare Research and Quality report on care coordination instruments, we conducted a structured literature review in search of more recent measures of care coordination, described in the appendix. The process uncovered a number of process-oriented and survey-based care coordination measures. [20] [21] [22] However, of these new measures, only one aims to estimate a patient's need for care coordination, the Care Coordination Tier Assessment Tool. 23, 24 However, this instrument requires individual assessment by a person reviewing a patient's case, and this person must be able to account for duration of their conditions and the care team available to them. This would not be possible for researchers who only have access to large databases of hospital discharges or insurer claims. Our Specialty Intensity Score is intended for use by health services researchers who work with widely available datasets, like the Agency for Healthcare Research and Quality National Inpatient Sample database or state-level hospital discharge datasets. Because the Specialty Intensity Score is a version of a comorbidity index, we wanted to compare it to existing such measures, particularly those constructed using similar data. We therefore conducted a structured literature review in search of existing comorbidity instruments. Our findings appear in Table 1 , and the process for identifying these measures is described in an appendix. All measures in the table are instruments that aggregate patient comorbidities into scores that predict patient outcome or usage patterns. None of the existing comorbidity scores was proxies for the amount of coordination patients would need, and none used number of unique physician specialties as the response variable of interest. Most of the scores were constructed using mortality or prognosis as the main response variable, with various patient bases and techniques for score design. The most commonly cited such score is the Charlson Comorbidity Index. 27 A number of the scores were utilization based, but measured utilization by cost, hospitalization, readmission, or resource use, rather than by number of medical specialties involved in a patient's care. No existing comorbidity instrument was explicitly designed to estimate the complexity of a patient's interaction with the health care system, like Specialty Intensity Score that we develop in this article. In the sections that follow, we describe the process for developing the Specialty Intensity Score, by using ICD-10-CM codes to estimate the number of doctors with unique specialties a patient sees. We then test whether the Specialty Intensity Score is empirically different from the commonly used Charlson Comorbidity Index by quantifying the predictive value of each in estimating a patient's probability of experiencing a patient safety incident in the hospital. Our results show that the two scores are sufficiently distinct, and that the Specialty Intensity Score has higher predictive power for safety incidents. We utilized the Agency for Healthcare Research and Quality's Patient Safety Indicators 51 to estimate the number of medical complications each patient experienced while in the hospital. Table 2 gives the list of Patient Safety Indicators that we included in the study. Table 3 provides descriptive statistics on the prevalence of patient safety events, as observed in our data. To predict the average number of specialist doctors utilized with the diagnosis subchapter indicator variables, the first stage of our analysis employed a least absolute shrinkage and selection operator (LASSO) penalized Poisson model ( collinear. We carry out this entire process twice: once for the SI score created using the lower bound specialty count variable and once for the SI score created using the upper bound. Our analysis yielded two results. The first is the generation of the Specialty Intensity Score, which is a measure that we designed for health services researchers to utilize in their own studies, particularly studies that involve multiple chronic condition patients. The second result of our study estimates the power of the Specialty Intensity Score for predicting the occurrence of a negative patient safety event during a hospital stay, and the comparison of that score to the existing Charlson Comorbidity Score. Researchers can create SI Scores for patients in their own data using There are several assumptions that we had to make when constructing our measure. First, we assume that patients saw a doctor from every medical specialty that they needed expertise from. This assumption could be violated by good care coordination practices that some organizations engage in. For example, if a patient's primary care doctor made phone calls to specialists to get advice for that patient, then that patient would not appear to have consulted these doctors in our dataset, and we would underestimate the number of areas of medical expertise they needed. Conversely, we assumed that patients needed special medical expertise from every doctor that they saw. It is possible that some of these doctors were checking in on the patients for routine medical check-ups, unrelated to their specialties. Finally, we were unable to observe which specialty the patient was utilizing. Doctors could list up to two specialties and we had no way of knowing which of the two specialties was relevant to the patient. To account for this, we devised a maximum estimate of doctors seen and a minimum estimate and compared these approaches, as described in previous sections. In this article, we have developed the Specialty Intensity Score, which uses ICD-10-CM codes to score a patient's need for medical expertise drawn from different, unique medical specialties. We have done this by using clusters of ICD-10-CM codes to predict the number of specialist doctors from unique specialties that a patient sees during a hospital visit. To test the validity of our Specialty Intensity Score, we checked whether our score had power in predicting a patient's probability of experiencing a patient safety event, and whether that predictive power was independent of the Charlson comorbidity index. Our measure has significantly more predictive power in estimating a patient's probability of a patient safety event compared to the existing Charlson comorbidity index. This finding validates the need for our score as an independent source of meaning for researchers exploring issues relating to complex hospital patients with many comorbidities. Ashley Hodgson had full access to all of the data in this study and takes complete responsibility for the integrity of the data and the accuracy of the data analysis. Ashley Hodgson affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained. The data that support the findings of this study are available from the CMS [https://www.cms.gov/Research-Statistics-Data-and-Systems/ Files-for-Order/LimitedDataSets]. Restrictions apply to the availability of these data, which were used under license for this study. 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